This work integrates artificial bee colony (ABC) and beesalgorithm (BA) to develop a twobees (TB) algorithm. Agents of TB are stochastically assigned to ABC and BA sub-swarms in each iteration according to their fit...
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This work integrates artificial bee colony (ABC) and beesalgorithm (BA) to develop a twobees (TB) algorithm. Agents of TB are stochastically assigned to ABC and BA sub-swarms in each iteration according to their fitness values. Consequently, the current healthier sub-swarm gains more agents to carry out its actions. Sub-swarm populations therefore vary ceaselessly during iterations, while either the ABC or BA sub-swarms may be superior to the other. Experiments are conducted on 23 benchmark functions. Results demonstrate that the TB performs better than or close to the ABC or BA winner. TB overcomes the poor performance of ABC and BA in handling particular problems.
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